The present invention, in some embodiments thereof, relates to image analysis and, more particularly, but not exclusively, to a method and system for cephalometric analysis.
Medical practitioners, such as orthodontists, maxillofacial surgeons, ear, nose and throat surgeons, and other physicians use cephalometry for diagnosis, prognosis and treatment planning Cephalometric analysis allows defining certain norms and anomalies of a skeletal, dental and soft tissue of the craniofacial complex. Cephalometric measures of individuals can be compared with norms for age, sex and population group. Generally, cephalometric analysis includes identification of specific landmarks on a roentgenogram (an X-ray image) of the head. By plotting lines on the image, and measuring various measures of these lines the medical practitioner evaluates growth and development of anatomic structures. A comparison of the measures to previously acquired control group measures (e.g., normal populations of similar age, gender and ethnic group) allows the practitioner to diagnose bony and soft tissue anatomical variants and anomalies.
Cephalometric analysis has also been proposed as a tool for diagnosing sleep-disordered breathing (SDB) [Finkelstein et al., “Frontal and lateral cephalometry in patients with sleep-disordered breathing,” The Laryngoscope 111, 4:623-641 (2001)]. Lateral and frontal cephalometric radiographs were analyzed in a series of normal patients and those with varying degrees of SDB, and the degrees of narrowing or other unfavorable anatomical changes that may differentiate SDB subjects from normal subjects. SDB was found to be associated with statistically significant changes in several cephalometric measurements.
Additional background art includes Hoekema et al., “Craniofacial morphology and obstructive sleep apnoea: a cephalometric analysis,” J Oral Rehabil., 2003, 30(7):690-696; Maltais et al., “Cephalometric measurements in snorers, nonsnorers, and patients with sleep apnea,” Thorax, 1991, 46: 419-423; Sakakibara et al., “Cephalometric abnormalities in non-obese and obese patients with obstructive sleep apnoea,” Eur Respir J, 1999, 13:403-410; Mayer et al., “Relationship between body mass index, age and upper airway measurements in snorers and sleep apnea patients,” Eur Respir J, 1996, 9, 1801-1809; Fleisher et al., “Current Trends in the Treatment of Obstructive Sleep Apnea,” J Oral Maxillofac Surg 65:2056-2068, 2007; Battagel et al., “A cephalometric comparison of subjects with snoring and obstructive sleep apnea,” European Journal of Orthodontics 22, 2000, 353-365; Battagel et al., “Changes in airway and hyoid position in response to mandibular protrusion in subjects with obstructive sleep apnoea (OSA),” Eur J Orthod, 1999, 21 (4): 363-376; Hammond et al., “A follow-up study of dental and skeletal changes associated with mandibular advancement splint use in obstructive sleep apnea,” American Journal of Orthodontics and Dentofacial Orthopedics, Volume 132, 2007; Grybauskas et al., “Validity and reproducibility of cephalometric measurements obtained from digital photographs of analogue headfilms,” Stomatologija, Baltic Dental and Maxillofacial Journal, 9:114-120, 2007; Celik et al., “Comparison of cephalometric measurements with digital versus conventional cephalometric analysis,” Eur J Orthod, 2009, 31 (3): 241-246; Kim et al., “Pharyngeal airway changes after sagittal split ramus osteotomy of the mandible: a comparison between genders,” J Oral Maxillofac Surg., 2010, 68(8):1802-6; Kollias et al., “Adult craniocervical and pharyngeal changes—a longitudinal cephalometric study between 22 and 42 years of age. Part I: Morphological craniocervical and hyoid bone changes,” European Journal of Orthodontics, 1999, 21 (4):333-344; Cootes et al., “Active appearance models,” Pattern Analysis and Machine Intelligence, IEEE Transactions on 23(6), 2001, 681-685; Hutton et al., “An evaluation of active shape models for the automatic identification of cephalometric landmarks,” Eur. J. Orthodont, 22, 2000; Kafieh et al., “Automatic landmark detection in cephalometry using a modified active shape model with sub image matching,” ICMV07, 2007, 73-78, Rueda et al., “An approach for the automatic cephalometric landmark detection using mathematical morphology and AAM,” MICCAI, 2006, 159-166; and Yue et al., “Automated 2-d cephalometric analysis on x-ray images by a model-based approach,” IEEE. Tran. Biomed. Eng. 53(8), 2006.